Crude oil is an abundant energy source found throughout the world and exists in many different forms. In fact, there are over 200 different crude oils traded worldwide. In general, crude oils are complex mixtures of thousands of hydrocarbons, such as paraffins and aromatic hydrocarbons, which can be classified by their density (e.g., API gravity). For example, “light” crude oil has a low density and “heavy” crude oil has a high density.
While there are many different types of crude oils, not all crude oils perform the same way in a given refinery. As such, refineries are constantly trying to find the optimal crude oil for the products they aim to produce and for the operating conditions of the different units of the refinery. Conventionally, refineries attempt to optimize their crude oil slate using essentially trial and error methods. More specifically, refineries typically select their crude oil slate using a variety of factors including worldwide supply and demand (availability and price), refinery capability and configuration, transportation costs, and refining costs. After selecting the crude oil slate, refineries then evaluate the cost effectiveness of that crude oil slate on their processing units. Further, due to market conditions, refineries do not always received the same crude oil. As such, the impact of the crude oil slate on the processing units combined with the market conditions force refineries to frequently reevaluate their crude oil slate. Because current methods do not allow the refineries to predict the effect that a particular crude oil will have on its processing units, refineries continue to use trial and error methods to best determine the optimal crude oil for their processing units.
As such, there is a need for a way to predict the impact of a particular crude oil composition (e.g., a crude oil blend) on processing units to allow for more efficient crude oil slate optimization and to allow the selection of optimal operating conditions for a given crude oil slate.